Ocean color data is crucial for understanding biological and ecological processes, and is also an important input source for ocean physics and biogeochemical models. In the marine environment, chlorophyll-a (Chl-a) is a key variable for ocean color. Considering the environmental variables related to the growth and distribution of marine phytoplankton, a convolutional neural network (CNN) called OCNET was developed for reconstructing Chl-a concentration data in open sea areas.
Further analyze data and satellite observations of sea surface temperature (SST), salinity (SAL), photosynthetically active radiation (PAR), and sea surface pressure (SSP).
OCNET uses reanalysis data and satellite observations of sea surface temperature (SST), salinity (SAL), photosynthetically active radiation (PAR), and sea surface pressure (SSP) as inputs to correlate with the environment and phytoplankton mass. The established OCNET model has achieved good results in the reconstruction of global ocean Chl-a concentration data and captured the temporal variations of these features.
The data quality is good.
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CSTR:11738.11.NCDC.ZENODO.DB6671.2024
10.5281/zenodo.14177611